Dynamic multi-objective optimization algorithm based on individual prediction

A dynamic multi-objective optimization algorithm based on individual prediction (IPS) was proposed to quickly track the Pareto optimal front of the dynamic multi-objective optimization problem that changed with the environment. Firstly, the special points with good convergence and diversity were selected by the reference point relation algorithm, and the environment changes can be quickly responded to by predicting the special points set. Secondly, a feedback correction mechanism for population center point predication was proposed, and in the process of predicting the non-dominant solution set, the prediction step size was corrected to make the prediction more accurate. Finally, to avoid the algorithm falling into local optimal, a hybrid diversity maintenance mechanism was proposed, which introduced random individuals generated by Latin hypercube sampling and a precision controllable mutation strategy to improve the diversity of the population. The proposed algorithm was compared with the other four dynamic multi-objective optimization algorithms. Experimental results show that IPS can balance the diversity and convergence of the population, and the experimental results are better than that of the other four algorithms on the FDA, DMOP, and F5~F10 test suite. © 2023 Zhejiang University. All rights reserved.